Page 20 - Building Big Data Applications

14 Building Big Data Applications

Sensor Data Analytics: A new dimension of analytics is sensor data analytics.
Sensor data is continuous and always streaming. It can be generated from an
Apple iWatch, Samsung smartphone, Apple iPad, a smart wearable device, or a
BMW i series, Tesla, or hybrid car. How do we monetize from this data? The
answer is by implementing the appropriate sensor analytics programs. These pro-
grams require a team of subject and analytics experts to come together in a data
science team approach for meeting the challenges and providing directions to the
outcomes in the Internet of Things world. This move has started in many organiza-
tions but lacks direction and needs a chief analytics officer or chief data officer role
to make it work in reality.

Servers,
Sensors Webmail, Images Video files
System log files

Audio files Social Media Data files

Machine Intelligence: This success factor refers to an ecosystem of analytics and
actions built on system outcomes from machines. These machines work 24/7/365
and can process data in continuum, which requires a series of algorithms, pro-
cesses, code, analytics, action-driven outcomes, and no human interference. Work
taking place for more than 25 years in this area has led to outcomes such as IBM
Watson; TensorFlow, an open source library for numeric computation; Bayesian
networks; hidden Markov model (HMM) algorithms; and Decision theory and
Utility theory models of Web 3.0 processing. This field is the advancement of artifi-
cial intelligence algorithms and has more research and advancement published by
Apache Software Foundation, Google, IBM and many universities.
Graph Databases: In the world of the Internet of Things, graph databases repre-
sent the most valuable data processing infrastructure. This infrastructure exists
because data will be streaming constantly and be processed by machines and peo-
ple. It requires nodes of processing across infrastructure and algorithms with data
captured, ingested, processed, and analyzed. Graph databases can scale up and out
in these situations, and they can process with in-memory architectures such as
Apache Spark, which provides a good platform for this new set of requirements.
Algorithms: The algorithm success factor holds the keys to the castle in the world
of the Internet of Things. Several algorithms are available, and they can be imple-
mented across all layers of this ecosystem.